{"id":"W2026499148","doi":"10.1109/mwscas.2010.5548912","title":"Ill condition identification for linear prediction-based MIMO-OFDM channel estimation","year":2010,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"MIMO; Algorithm; Channel (broadcasting); Orthogonal frequency-division multiplexing; MIMO-OFDM; Computer science; SIGNAL (programming language); Control theory (sociology); Identification (biology); Linear prediction; Mathematics; Mathematical optimization; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009253799,0.0007410962,0.0007291089,0.0004862322,0.0004802798,0.0006428423,0.0006151954,0.000817412,0.001309891],"category_scores_gemma":[0.005321099,0.0003483149,0.0004003585,0.0004221876,0.001006596,0.001463532,0.0009906952,0.001318199,0.000594695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003612404,"about_ca_system_score_gemma":0.0009093778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009067903,"about_ca_topic_score_gemma":0.0009969367,"domain_scores_codex":[0.999028,0.0002931823,0.00004866839,0.00012649,0.0004437463,0.00005991798],"domain_scores_gemma":[0.9975331,0.001401113,0.0002695292,0.000287891,0.0004468663,0.0000614765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006896922,0.0001725802,0.002337571,0.0003052874,0.00009366257,0.000268077,0.0003315004,0.4304109,0.08501127,0.03783407,0.003275882,0.4392696],"study_design_scores_gemma":[0.000009629291,0.00003825473,0.0002005792,0.000005380381,0.000005827519,0.0000732276,0.000005996596,0.9838997,0.01319743,0.002176947,0.000369565,0.00001739398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003398475,0.0000333103,0.9961432,0.00002164023,0.00001101317,0.00001080191,0.000007290186,0.000176623,0.0001978084],"genre_scores_gemma":[0.345712,0.0002574531,0.65218,0.00009559563,0.00007190555,0.0001589408,0.0001285702,0.00007406747,0.001321442],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001309891,"threshold_uncertainty_score":0.004893899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01614495554167841,"score_gpt":0.2926283082155677,"score_spread":0.2764833526738893,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}